Very Large Cystoid Macular Lesions Identified Using Outlier Analysis of Genetically Confirmed Inherited Retinal Disease Cases
Bibliographic record
Abstract
Background: Cystoid macular lesions (CML) in inherited retinal diseases (IRDs) can contribute to vision impairment.Studying the morphologic range and outlier presentations of CML may inform clinical associations, mechanistic research, and trial design.Thus, we aim to describe the distribution of optical coherence tomography (OCT) parameters in IRD cases with CML and identify phenotype-genotype associations in very large cystoid macular lesions (VLCML).Materials and methods: This cross-sectional study retrieved clinical information from electronic records from January 2020 to December 2021.VLCML cases were identified using the robust distance (Mahalanobis) of the correlation between central foveal thickness (CFT) and total macular volume (TMV) and a 99.9% probability ellipse.The distribution of OCT parameters was calculated by genotype and phenotype.Results: We included 173 eyes of 103 subjects.The median age was 55.9 (interquartile range [IQR], 37.9, 63.7) and 47.6% (49/103) were females.Patients had disease-causing mutations in 30 genes.The most common genes included USH2A (n = 18), RP1 (n = 12), and ABCA4 (n = 11).Robust distance analysis showed that the prevalence of VLCML was 1.94% (n = 2 patients, 4 eyes).VLCML was seen in cases of NR2E3 (119-2A>C) and BEST1 (1120_1121insG) mutations.The median CFT in cases without VLCML was 269 µm (IQR 209, 318.50) while the median for VLCML cases was 1,490 µm (IQR 1,445.50,1,548.00)(P < .001).Conclusions: Subjects with different IRD genotypes may develop VLCMLs.Future studies could consider the range and outlier values of CML foveal thickness when determining inclusion criteria and biostatistical plans for observational and interventional studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".